/usr/lib/python2.7/dist-packages/pyFAI/distortion.py is in pyfai 0.10.2-1.
This file is owned by root:root, with mode 0o644.
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# -*- coding: utf-8 -*-
#
# Project: Azimuthal integration
# https://github.com/kif/pyFAI
#
# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
__author__ = "Jérôme Kieffer"
__contact__ = "Jerome.Kieffer@ESRF.eu"
__license__ = "GPLv3+"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "29/09/2014"
__status__ = "development"
import logging, threading
import types, os, sys
import numpy
logger = logging.getLogger("pyFAI.distortion")
logging.basicConfig(level=logging.INFO)
from math import ceil, floor
from pyFAI import detectors, ocl_azim_lut, ocl_azim_csr
from pyFAI.utils import timeit
import fabio
try:
from . import _distortion
except ImportError:
logger.warning("Import _distortion cython implementation failed ... pure python version is terribly slow !!!")
_distortion = None
class Distortion(object):
"""
This class applies a distortion correction on an image.
New version compatible both with CSR and LUT...
"""
def __init__(self, detector="detector", shape=None, method="LUT", device=None, workgroup=8):
"""
@param detector: detector instance or detector name
@param shape: shape of the output image
@param method: "lut" or "csr", the former is faster
@param device: Name of the device: None for OpenMP, "cpu" or "gpu" or the id of the OpenCL device a 2-tuple of integer
@param workgroup: workgroup size for CSR on OpenCL
"""
if type(detector) in types.StringTypes:
self.detector = detectors.detector_factory(detector)
else: # we assume it is a Detector instance
self.detector = detector
if "shape" in dir(self.detector):
self.shape = self.detector.shape
if shape is not None and self.shape != shape:
logger.warning("unconsistency in detector geometry, got %s and expected %s" % (shape, self.shape))
self.shape = shape
else:
self.shape = shape
self.shape = tuple([int(i) for i in self.shape])
self._sem = threading.Semaphore()
self.bin_size = None
self.max_size = None
self.pos = None
self.lut = None
self.delta0 = self.delta1 = None # max size of an pixel on a regular grid ...
self.integrator = None
if not method:
self.method = "lut"
else:
self.method = method.lower()
self.device = device
if not workgroup:
self.workgroup = 8
else:
self.workgroup = int(workgroup)
def __repr__(self):
return os.linesep.join(["Distortion correction %s on device %s for detector shape %s:" % (self.method, self.device, self.shape),
self.detector.__repr__()])
def reset(self, method=None, device=None, workgroup=None):
"""
reset the distortion correction and re-calculate the look-up table
@param method: can be "lut" or "csr", "lut" looks faster
@param device: can be None, "cpu" or "gpu" or the id as a 2-tuple of integer
@param worgroup: enforce the workgroup size for CSR.
"""
with self._sem:
self.max_size = None
self.pos = None
self.lut = None
self.delta0 = self.delta1 = None
self.integrator = None
if method is not None:
self.method = method.lower()
if device is not None:
self.device = device
if workgroup is not None:
self.workgroup = int(workgroup)
self.calc_init()
@timeit
def calc_pos(self):
if self.delta1 is None:
with self._sem:
if self.delta1 is None:
pos_corners = numpy.empty((self.shape[0] + 1, self.shape[1] + 1, 2), dtype=numpy.float64)
d1 = numpy.outer(numpy.arange(self.shape[0] + 1, dtype=numpy.float64), numpy.ones(self.shape[1] + 1, dtype=numpy.float64)) - 0.5
d2 = numpy.outer(numpy.ones(self.shape[0] + 1, dtype=numpy.float64), numpy.arange(self.shape[1] + 1, dtype=numpy.float64)) - 0.5
pos_corners[:, :, 0], pos_corners[:, :, 1] = self.detector.calc_cartesian_positions(d1, d2)
pos_corners[:, :, 0] /= self.detector.pixel1
pos_corners[:, :, 1] /= self.detector.pixel2
pos = numpy.empty((self.shape[0], self.shape[1], 4, 2), dtype=numpy.float32)
pos[:, :, 0, :] = pos_corners[:-1, :-1]
pos[:, :, 1, :] = pos_corners[:-1, 1: ]
pos[:, :, 2, :] = pos_corners[1: , 1: ]
pos[:, :, 3, :] = pos_corners[1: , :-1]
self.pos = pos
self.delta0 = int((numpy.ceil(pos_corners[1:, :, 0]) - numpy.floor(pos_corners[:-1, :, 0])).max())
self.delta1 = int((numpy.ceil(pos_corners[:, 1:, 1]) - numpy.floor(pos_corners[:, :-1, 1])).max())
return self.pos
@timeit
def calc_size(self):
"""
Considering the "half-CCD" spline from ID11 which describes a (1025,2048) detector,
the physical location of pixels should go from:
[-17.48634 : 1027.0543, -22.768829 : 2028.3689]
We chose to discard pixels falling outside the [0:1025,0:2048] range with a lose of intensity
"""
if self.pos is None:
pos = self.calc_pos()
else:
pos = self.pos
if self.max_size is None:
with self._sem:
if self.max_size is None:
if _distortion:
self.bin_size = _distortion.calc_size(self.pos, self.shape)
else:
pos0min = numpy.floor(pos[:, :, :, 0].min(axis= -1)).astype(numpy.int32).clip(0, self.shape[0])
pos1min = numpy.floor(pos[:, :, :, 1].min(axis= -1)).astype(numpy.int32).clip(0, self.shape[1])
pos0max = (numpy.ceil(pos[:, :, :, 0].max(axis= -1)).astype(numpy.int32) + 1).clip(0, self.shape[0])
pos1max = (numpy.ceil(pos[:, :, :, 1].max(axis= -1)).astype(numpy.int32) + 1).clip(0, self.shape[1])
self.bin_size = numpy.zeros(self.shape, dtype=numpy.int32)
max0 = 0
max1 = 0
for i in range(self.shape[0]):
for j in range(self.shape[1]):
if (pos0max[i, j] - pos0min[i, j]) > max0:
old = max0
max0 = pos0max[i, j] - pos0min[i, j]
print old, "new max0", max0, i, j
if (pos1max[i, j] - pos1min[i, j]) > max1:
old = max1
max1 = pos1max[i, j] - pos1min[i, j]
print old, "new max1", max1, i, j
self.bin_size[pos0min[i, j]:pos0max[i, j], pos1min[i, j]:pos1max[i, j]] += 1
self.max_size = self.bin_size.max()
def calc_init(self):
"""
initialize all arrays
"""
self.calc_pos()
self.calc_size()
self.calc_LUT()
if self.device is not None:
if "lower" in dir(self.device):
self.device = self.device.lower()
if self.method == "lut":
self.integrator = ocl_azim_lut.OCL_LUT_Integrator(self.lut, self.shape[0] * self.shape[1], devicetype=self.device)
else:
self.integrator = ocl_azim_csr.OCL_CSR_Integrator(self.lut, self.shape[0] * self.shape[1], devicetype=self.device,
block_size=self.workgroup)
else:
if self.method == "lut":
self.integrator = ocl_azim_lut.OCL_LUT_Integrator(self.lut, self.shape[0] * self.shape[1],
platformid=self.device[0], deviceid=self.device[1])
else:
self.integrator = ocl_azim_csr.OCL_CSR_Integrator(self.lut, self.shape[0] * self.shape[1],
platformid=self.device[0], deviceid=self.device[1],
block_size=self.workgroup)
@timeit
def calc_LUT(self):
if self.max_size is None:
self.calc_size()
if self.lut is None:
with self._sem:
if self.lut is None:
if _distortion:
if self.method == "lut":
self.lut = _distortion.calc_LUT(self.pos, self.shape, self.bin_size, max_pixel_size=(self.delta0, self.delta1))
else:
self.lut = _distortion.calc_CSR(self.pos, self.shape, self.bin_size, max_pixel_size=(self.delta0, self.delta1))
else:
lut = numpy.recarray(shape=(self.shape[0] , self.shape[1], self.max_size), dtype=[("idx", numpy.uint32), ("coef", numpy.float32)])
lut[:, :, :].idx = 0
lut[:, :, :].coef = 0.0
outMax = numpy.zeros(self.shape, dtype=numpy.uint32)
idx = 0
buffer = numpy.empty((self.delta0, self.delta1))
quad = Quad(buffer)
for i in range(self.shape[0]):
for j in range(self.shape[1]):
# i,j, idx are indexes of the raw image uncorrected
quad.reinit(*list(self.pos[i, j, :, :].ravel()))
# print self.pos[i, j, 0, :], self.pos[i, j, 1, :], self.pos[i, j, 2, :], self.pos[i, j, 3, :]
try:
quad.populate_box()
except Exception as error:
print "error in quad.populate_box of pixel %i, %i: %s" % (i, j, error)
print "calc_area_vectorial", quad.calc_area_vectorial()
print self.pos[i, j, 0, :], self.pos[i, j, 1, :], self.pos[i, j, 2, :], self.pos[i, j, 3, :]
print quad
raise
# box = quad.get_box()
for ms in range(quad.get_box_size0()):
ml = ms + quad.get_offset0()
if ml < 0 or ml >= self.shape[0]:
continue
for ns in range(quad.get_box_size1()):
# ms,ns are indexes of the corrected image in short form, ml & nl are the same
nl = ns + quad.get_offset1()
if nl < 0 or nl >= self.shape[1]:
continue
val = quad.get_box(ms, ns)
if val <= 0:
# print "Val ", val, i, j, idx, ms, ns, ml, nl
continue
k = outMax[ml, nl]
lut[ml, nl, k].idx = idx
lut[ml, nl, k].coef = val
outMax[ml, nl] = k + 1
idx += 1
lut.shape = (self.shape[0] * self.shape[1]), self.max_size
self.lut = lut
def correct(self, image):
"""
Correct an image based on the look-up table calculated ...
@param image: 2D-array with the image
@return: corrected 2D image
"""
if self.device:
if self.integrator is None:
self.calc_init()
out = self.integrator.integrate(image)[1]
else:
if self.lut is None:
self.calc_LUT()
if self.method == "lut":
if _distortion is not None:
out = _distortion.correct_LUT(image, self.shape, self.lut)
else:
big = image.ravel().take(self.lut.idx) * self.lut.coef
out = big.sum(axis= -1)
elif self.method == "csr":
if _distortion is not None:
out = _distortion.correct_CSR(image, self.shape, self.lut)
else:
big = self.lut[0] * image.ravel().take(self.lut[1])
indptr = self.lut[2]
out = numpy.zeros(indptr.size - 1)
for i in range(indptr.size - 1):
out[i] = big[indptr[i]:indptr[i + 1]].sum()
out.shape = self.shape
return out
def uncorrect(self, image):
"""
Take an image which has been corrected and transform it into it's raw (with loss of information)
@param image: 2D-array with the image
@return: uncorrected 2D image and a mask (pixels in raw image
"""
if self.lut is None:
self.calc_LUT()
if self.method == "lut":
if _distortion is not None:
out, mask = _distortion.uncorrect_LUT(image, self.shape, self.lut)
else:
out = numpy.zeros(self.shape, dtype=numpy.float32)
mask = numpy.zeros(self.shape, dtype=numpy.int8)
lmask = mask.ravel()
lout = out.ravel()
lin = image.ravel()
tot = self.lut.coef.sum(axis= -1)
for idx in range(self.lut.shape[0]):
t = tot[idx]
if t <= 0:
lmask[idx] = 1
continue
val = lin[idx] / t
lout[self.lut[idx].idx] += val * self.lut[idx].coef
elif self.method == "csr":
if _distortion is not None:
out = _distortion.uncorrect_CSR(image, self.shape, self.lut)
else:
raise NotImplementedError()
return out, mask
class Quad(object):
"""
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"""
def __init__(self, buffer):
self.box = buffer
self.A0 = self.A1 = None
self.B0 = self.B1 = None
self.C0 = self.C1 = None
self.D0 = self.D1 = None
self.offset0 = self.offset1 = None
self.box_size0 = self.box_size1 = None
self.pAB = self.pBC = self.pCD = self.pDA = None
self.cAB = self.cBC = self.cCD = self.cDA = None
self.area = None
def get_idx(self, i, j):
pass
def get_box(self, i, j):
return self.box[i, j]
def get_offset0(self):
return self.offset0
def get_offset1(self):
return self.offset1
def get_box_size0(self):
return self.box_size0
def get_box_size1(self):
return self.box_size1
def reinit(self, A0, A1, B0, B1, C0, C1, D0, D1):
self.box[:, :] = 0.0
self.A0 = A0
self.A1 = A1
self.B0 = B0
self.B1 = B1
self.C0 = C0
self.C1 = C1
self.D0 = D0
self.D1 = D1
self.offset0 = int(floor(min(self.A0, self.B0, self.C0, self.D0)))
self.offset1 = int(floor(min(self.A1, self.B1, self.C1, self.D1)))
self.box_size0 = int(ceil(max(self.A0, self.B0, self.C0, self.D0))) - self.offset0
self.box_size1 = int(ceil(max(self.A1, self.B1, self.C1, self.D1))) - self.offset1
self.A0 -= self.offset0
self.A1 -= self.offset1
self.B0 -= self.offset0
self.B1 -= self.offset1
self.C0 -= self.offset0
self.C1 -= self.offset1
self.D0 -= self.offset0
self.D1 -= self.offset1
self.pAB = self.pBC = self.pCD = self.pDA = None
self.cAB = self.cBC = self.cCD = self.cDA = None
self.area = None
def __repr__(self):
return os.linesep.join(["offset %i,%i size %i, %i" % (self.offset0, self.offset1, self.box_size0, self.box_size1), "box: %s" % self.box[:self.box_size0, :self.box_size1]])
def init_slope(self):
if self.pAB is None:
if self.B0 == self.A0:
self.pAB = numpy.inf
else:
self.pAB = (self.B1 - self.A1) / (self.B0 - self.A0)
if self.C0 == self.B0:
self.pBC = numpy.inf
else:
self.pBC = (self.C1 - self.B1) / (self.C0 - self.B0)
if self.D0 == self.C0:
self.pCD = numpy.inf
else:
self.pCD = (self.D1 - self.C1) / (self.D0 - self.C0)
if self.A0 == self.D0:
self.pDA = numpy.inf
else:
self.pDA = (self.A1 - self.D1) / (self.A0 - self.D0)
self.cAB = self.A1 - self.pAB * self.A0
self.cBC = self.B1 - self.pBC * self.B0
self.cCD = self.C1 - self.pCD * self.C0
self.cDA = self.D1 - self.pDA * self.D0
def calc_area_AB(self, I1, I2):
if numpy.isfinite(self.pAB):
return 0.5 * (I2 - I1) * (self.pAB * (I2 + I1) + 2 * self.cAB)
else:
return 0
def calc_area_BC(self, J1, J2):
if numpy.isfinite(self.pBC):
return 0.5 * (J2 - J1) * (self.pBC * (J1 + J2) + 2 * self.cBC)
else:
return 0
def calc_area_CD(self, K1, K2):
if numpy.isfinite(self.pCD):
return 0.5 * (K2 - K1) * (self.pCD * (K2 + K1) + 2 * self.cCD)
else:
return 0
def calc_area_DA(self, L1, L2):
if numpy.isfinite(self.pDA):
return 0.5 * (L2 - L1) * (self.pDA * (L1 + L2) + 2 * self.cDA)
else:
return 0
def calc_area_old(self):
if self.area is None:
if self.pAB is None:
self.init_slope()
self.area = -self.calc_area_AB(self.A0, self.B0) - \
self.calc_area_BC(self.B0, self.C0) - \
self.calc_area_CD(self.C0, self.D0) - \
self.calc_area_DA(self.D0, self.A0)
return self.area
def calc_area_vectorial(self):
if self.area is None:
self.area = numpy.cross([self.C0 - self.A0, self.C1 - self.A1], [self.D0 - self.B0, self.D1 - self.B1]) / 2.0
return self.area
calc_area = calc_area_vectorial
def populate_box(self):
if self.pAB is None:
self.init_slope()
self.integrateAB(self.B0, self.A0, self.calc_area_AB)
self.integrateAB(self.A0, self.D0, self.calc_area_DA)
self.integrateAB(self.D0, self.C0, self.calc_area_CD)
self.integrateAB(self.C0, self.B0, self.calc_area_BC)
if (self.box / self.calc_area()).min() < 0:
print self.box
self.box[:, :] = 0
print "AB"
self.integrateAB(self.B0, self.A0, self.calc_area_AB)
print self.box
self.box[:, :] = 0
print "DA"
self.integrateAB(self.A0, self.D0, self.calc_area_DA)
print self.box
self.box[:, :] = 0
print "CD"
self.integrateAB(self.D0, self.C0, self.calc_area_CD)
print self.box
self.box[:, :] = 0
print "BC"
self.integrateAB(self.C0, self.B0, self.calc_area_BC)
print self.box
print self
raise RuntimeError()
self.box /= self.calc_area_vectorial()
def integrateAB(self, start, stop, calc_area):
h = 0
# print start, stop, calc_area(start, stop)
if start < stop: # positive contribution
P = ceil(start)
dP = P - start
# print "Integrate", start, P, stop, calc_area(start, stop)
if P > stop: # start and stop are in the same unit
A = calc_area(start, stop)
if A != 0:
AA = abs(A)
sign = A / AA
dA = (stop - start) # always positive
# print AA, sign, dA
h = 0
while AA > 0:
if dA > AA:
dA = AA
AA = -1
self.box[int(floor(start)), h] += sign * dA
AA -= dA
h += 1
else:
if dP > 0:
A = calc_area(start, P)
if A != 0:
AA = abs(A)
sign = A / AA
h = 0
dA = dP
while AA > 0:
if dA > AA:
dA = AA
AA = -1
self.box[int(floor(P)) - 1, h] += sign * dA
AA -= dA
h += 1
# subsection P1->Pn
for i in range(int(floor(P)), int(floor(stop))):
A = calc_area(i, i + 1)
if A != 0:
AA = abs(A)
sign = A / AA
h = 0
dA = 1.0
while AA > 0:
if dA > AA:
dA = AA
AA = -1
self.box[i , h] += sign * dA
AA -= dA
h += 1
# Section Pn->B
P = floor(stop)
dP = stop - P
if dP > 0:
A = calc_area(P, stop)
if A != 0:
AA = abs(A)
sign = A / AA
h = 0
dA = abs(dP)
while AA > 0:
if dA > AA:
dA = AA
AA = -1
self.box[int(floor(P)), h] += sign * dA
AA -= dA
h += 1
elif start > stop: # negative contribution. Nota is start=stop: no contribution
P = floor(start)
if stop > P: # start and stop are in the same unit
A = calc_area(start, stop)
if A != 0:
AA = abs(A)
sign = A / AA
dA = (start - stop) # always positive
h = 0
while AA > 0:
if dA > AA:
dA = AA
AA = -1
self.box[int(floor(start)), h] += sign * dA
AA -= dA
h += 1
else:
dP = P - start
if dP < 0:
A = calc_area(start, P)
if A != 0:
AA = abs(A)
sign = A / AA
h = 0
dA = abs(dP)
while AA > 0:
if dA > AA:
dA = AA
AA = -1
self.box[int(floor(P)) , h] += sign * dA
AA -= dA
h += 1
# subsection P1->Pn
for i in range(int(start), int(ceil(stop)), -1):
A = calc_area(i, i - 1)
if A != 0:
AA = abs(A)
sign = A / AA
h = 0
dA = 1
while AA > 0:
if dA > AA:
dA = AA
AA = -1
self.box[i - 1 , h] += sign * dA
AA -= dA
h += 1
# Section Pn->B
P = ceil(stop)
dP = stop - P
if dP < 0:
A = calc_area(P, stop)
if A != 0:
AA = abs(A)
sign = A / AA
h = 0
dA = abs(dP)
while AA > 0:
if dA > AA:
dA = AA; AA = -1
self.box[int(floor(stop)), h] += sign * dA
AA -= dA
h += 1
def test():
import numpy
buffer = numpy.empty((20, 20), dtype=numpy.float32)
Q = Quad(buffer)
Q.reinit(7.5, 6.5, 2.5, 5.5, 3.5, 1.5, 8.5, 1.5)
Q.init_slope()
# print Q.calc_area_AB(Q.A0, Q.B0)
# print Q.calc_area_BC(Q.B0, Q.C0)
# print Q.calc_area_CD(Q.C0, Q.D0)
# print Q.calc_area_DA(Q.D0, Q.A0)
print Q.calc_area()
Q.populate_box()
print Q
# print Q.get_box().sum()
print buffer.sum()
print("#"*50)
Q.reinit(8.5, 1.5, 3.5, 1.5, 2.5, 5.5, 7.5, 6.5)
Q.init_slope()
# print Q.calc_area_AB(Q.A0, Q.B0)
# print Q.calc_area_BC(Q.B0, Q.C0)
# print Q.calc_area_CD(Q.C0, Q.D0)
# print Q.calc_area_DA(Q.D0, Q.A0)
print Q.calc_area()
Q.populate_box()
print Q
# print Q.get_box().sum()
print buffer.sum()
Q.reinit(0.9, 0.9, 0.8, 6.9, 4.3, 3.9, 4.3, 0.9)
# Q = Quad((-0.3, 1.9), (-0.4, 2.9), (1.3, 2.9), (1.3, 1.9))
Q.init_slope()
print "calc_area_vectorial", Q.calc_area()
# print Q.A0, Q.A1, Q.B0, Q.B1, Q.C0, Q.C1, Q.D0, Q.D1
# print "Slope", Q.pAB, Q.pBC, Q.pCD, Q.pDA
# print Q.calc_area_AB(Q.A0, Q.B0), Q.calc_area_BC(Q.B0, Q.C0), Q.calc_area_CD(Q.C0, Q.D0), Q.calc_area_DA(Q.D0, Q.A0)
print Q.calc_area()
Q.populate_box()
print buffer.T
# print Q.get_box().sum()
print Q.calc_area()
print("#"*50)
import fabio, numpy
# workin on 256x256
# x, y = numpy.ogrid[:256, :256]
# grid = numpy.logical_or(x % 10 == 0, y % 10 == 0) + numpy.ones((256, 256), numpy.float32)
# det = detectors.FReLoN("frelon_8_8.spline")
# # working with halfccd spline
x, y = numpy.ogrid[:1024, :2048]
grid = numpy.logical_or(x % 100 == 0, y % 100 == 0) + numpy.ones((1024, 2048), numpy.float32)
det = detectors.FReLoN("halfccd.spline")
# working with halfccd spline
# x, y = numpy.ogrid[:2048, :2048]
# grid = numpy.logical_or(x % 100 == 0, y % 100 == 0).astype(numpy.float32) + numpy.ones((2048, 2048), numpy.float32)
# det = detectors.FReLoN("frelon.spline")
print det, det.max_shape
dis = Distortion(det)
print dis
lut = dis.self.calc_size()
print dis.lut_size
print lut.mean()
dis.calc_LUT()
out = dis.correct(grid)
fabio.edfimage.edfimage(data=out.astype("float32")).write("test_correct.edf")
print("*"*50)
# x, y = numpy.ogrid[:2048, :2048]
# grid = numpy.logical_or(x % 100 == 0, y % 100 == 0)
# det = detectors.FReLoN("frelon.spline")
# print det, det.max_shape
# dis = Distortion(det)
# print dis
# lut = dis.self.calc_size()
# print dis.lut_size
# print "LUT mean & max", lut.mean(), lut.max()
# dis.calc_LUT()
# out = dis.correct(grid)
# fabio.edfimage.edfimage(data=out.astype("float32")).write("test2048.edf")
from .gui_utils import pylab
pylab.imshow(out) # , interpolation="nearest")
pylab.show()
if __name__ == "__main__":
det = dis = lut = None
test()
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